Here's how you can measure and track the impact of your data analytics work on business outcomes.

Looking at the past is what helps you build your goals. There is no way to set goals if they are not achievable, you cannot set goals if they are not relevant, and there is also no way to set goals if you cannot define the time in which they must be achieved. Therefore, together with the team, the metrics that need to be improved must be aligned in order to direct efforts to achieve them. The goal needs to be measurable, and must be closely monitored, timing changes in the results achieved through indicators with data analysis.

Measuring improvement in business problems starts with knowing what to measure. It is critical that you and your team are clear on what metrics you're using to understand problems, including their strengths and weaknesses. When dealing with human behavior, developing and choosing the right metrics can be the most challenging part of the process. Once you know what you want to measure, define clear goals. For example, "Increase recurring revenue by 15% in Q2."

All business metrics can be classified into three broad categories: revenue, profitability and risk. To determine which category a specific metric falls into, consider who in the company trusts and requests this information. This understanding helps to define the necessary KPIs, making it easier to monitor and measure the results of your analyses. By aligning your metrics with business goals, you ensure your data insights are relevant and actionable. This alignment also simplifies measuring and comparing performance, making it easier to measure the impact of your analytics.

Effective data analytics requires well-defined, quantifiable business goals. Analytics goals should be in line with these aims to guarantee that the insights are applicable and useful. The creation of key performance indicators (KPIs) offers a standard by which data-driven strategies can be evaluated for their effectiveness in boosting revenue, enhancing customer satisfaction, or cutting expenses.

To measure and track the impact of your data analytics work on business outcomes, start by defining clear, specific goals and key performance indicators (KPIs) that align with your business objectives. Use baseline metrics to establish a starting point for comparison. Implement analytics tools that can track these KPIs over time, and ensure your data collection processes are accurate and consistent. Regularly review and analyze the data to identify trends and insights. Communicate the findings to stakeholders through reports and visualizations, highlighting how the analytics work has influenced business outcomes. Adjust your strategies as needed based on the insights gained to continuously improve your impact.

Once you have developed the correct metrics and defined your goals surrounding a business problem, you need to collect data. I find it most helpful to think of data in these types of problems in terms of lag and lead measures. If the goal is to increase recurring revenue (lag measure), you need to make sure you're collecting data on lead measures like number of new paid subscriptions, cancellations, new free accounts created, new website visitors, etc. It can be a real challenge to collect data that is representative of the population you're hoping to analyze without introducing bias. When preparing to analyze the data, remember to use appropriate randomization techniques to create balanced control and treatment groups.

Set clear goals before collecting data. Ensure accuracy, completeness, and timeliness. Clean and prepare data using tools. Consistent data collection enables accurate trend tracking over time.

Different problems will require different tools for analysis, but for beginners in data analytics I find it most useful to focus on Predictive vs. Causal vs. Descriptive questions. Articulate the question you are trying to answer and determine which category it falls under. If you're wondering if an ad campaign caused an increase in free accounts created, and if those free accounts led to more free subscribers, I would set up two causal experiments. If I was able to assign users to treatment and control groups in real time, I'd use an RCT, and if I was looking at historical data, I'd use a difference in difference model. Check out my page for future posts on what strategy to use, and when.

It's critical to evaluate the results of your data analytics work. To find patterns, trends, and correlations, apply statistical techniques and visualization software. To evaluate the efficacy of analytics-driven tactics, compare your KPIs pre- and post-implementation. This assessment offers concrete proof of how data analytics helps businesses achieve their objectives, supporting the validity of your analytics programs and directing future plans for ongoing development.

Data storytelling is a key part of this process. When you've been careful and thoughtful about how you've collected your data and set up experiments, you can present your findings with a realistic degree of confidence. Even then, I find it's always important to be very clear about potential lurking variables, data strengths and weaknesses, and risks associated with the analysis. Communication needs to be simplified and straightforward without stripping out the nuance that comes with statistics. This comes with practice and clarity on what potential pitfalls you're avoiding at each step of the process.

It's easy to think you've answered your question and that you're done with the data analytics process. Let's say you found your ad campaign led to an 18% increase in new accounts created and a 6% increase in paid subscribers. Now you need to run tests to see if you are running the best version of that ad campaign, and if you're running it on the right platform. Using the same strategies we use when we analyze data, we can consider how to best optimize the results of our experiment. By boiling down our desires into the right questions, we can use A/B tests, randomized experiments, and other analytics tools move closer to the business goal we defined at the beginning of the process.

Establishing a culture that prioritizes decision-making based on data is crucial for long-term company success. Urge all interested parties to actively participate in data insights and recognize their significance for strategic planning. You can make sure that analytics are regularly used to guide strategies and actions by encouraging a culture where data is essential to every decision. This culture change makes data the central component of your company's operations, resulting in better decisions, better results, and ongoing development in all business domains.